TY  - SER
AU  - Thet Htar Nyo
AU  - Xue,Wenchao
AU  - Cruz,Simon Guerrero
AU  - Ekbordin Winijkul,
AU  - Tsusaka,Takuji W.
ED  - AIT Scholarship,
TI  - A machine learning approach to localizing methane emission factors in rice cultivation: a case study in Ayutthaya, Thailand
T2  - Thesis
PY  - 2025///
CY  - Pathum Thani, Thailand
PB  - Asian Institute of Technology
KW  - Rice
KW  - Planting
KW  - Environmental aspects
KW  - Thailand
KW  - Data processing
KW  - Machine learning
N1  - A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Environmental Engineering and Management; Thesis (M. Eng.) - Asian Institute of Technology, 2025
N2  - Methane (CH₄) emissions from rice cultivation significantly contribute to agricultural  greenhouse gas emissions. Conventional estimation practices in developing countries often  rely on generalized emission factors recommended by the Intergovernmental Panel on  Climate Change (IPCC), due to the lack of sufficient ground-based monitoring and  measurement. These estimations may not reflect the specific agricultural practices and  environmental conditions of local regions. This study aims to localize methane emission  factors for rice cultivation in Ayutthaya, Thailand, using a machine learning-based approach.  A Gradient Boosting regression model was developed and trained on a global literature  dataset containing known CH₄ emissions and associated agricultural variables. Key features  influencing CH₄ emissions; pH, water regime, crop duration, organic amendment, N amount,  and soil properties were selected based on feature importance analysis and SHAP (SHapley  Additive exPlanations) values. The trained model was then applied to a Thailand-specific  secondary dataset to predict methane emission factors.  Results show that the predicted average CH₄ emission factor for the Thailand dataset is  0.8518 kg CH₄/ha/day, which is significantly lower than the IPCC Tier 1 default value of  1.30 kg CH₄/ha/day. This suggests that the IPCC default value may substantially  overestimate methane emissions in certain local contexts, highlighting the importance of  developing localized emission factors for improved regional GHG assessments.  This research provides a practical framework for applying machine learning to improve  emission factor localization. The findings offer valuable insights for policymakers and  researchers in developing tailored mitigation strategies for rice agriculture in Southeast Asia.
UR  - http://203.159.5.9/ait-thesis/detail.php?q=B23006
ER  -